Elevated emotion network connectivity is associated with fluctuations in depression

Significance Theories posit that emotional network connectivity confers risk for depression. This is because in connected networks, when external events trigger sadness, they activate other emotions like guilt and irritability. However, it is not clear why network connectivity is related to worsening depression, rather than having it simply become more changeable We tested this by estimating personalized emotion networks in two independent samples over 8 wk. We found strong support that network connectivity is linked to depression variance, not severity. We replicated this in a third independent community sample. This work demonstrates that emotion network connectivity captures a system’s ability to change, rather than its state at any given time, providing an understanding of how depression varies across time.


TABLE S3: ASSOCIATION BETWEEN NETWORK CONNECTIVITY IN THE EXEMPLAR 5-NODE NETWORK WITH SD DEPRESSION (PAID STUDENTS & CITIZEN SCIENTISTS), SD GLOOMY (HNATD), AND SD DOWN OR DEPRESSED (CLINICAL SAMPLE).
12 FIGURE S1: ASSOCIATION OF PER-PARTICIPANT NETWORK CONNECTIVITY AND WEEK-8 DEPRESSION.

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depression using imputed data.We did this using the amelia package, which performs multiple (n=5) imputations of missing data in a multivariate time-series and averages across them to produce an imputed timeseries.
Permutation Tests.To test if these distributions had greater correlations than expected by chance, we performed permutation tests.Specifically, in all 5-node network combinations, we randomised the timeseries of each participant's EMA data.We estimated personalised contemporaneous and directed networks based on this randomised data for each of the 4,368 network combinations and determined the correlation between (randomised) network connectivity and depression for each.We determined the statistical significance for each network by counting the number of values in the shuffled distribution that exceeded the observed value for a given true network, i.e., its p-value.We repeated this for all combinations of networks and then found the proportion of networks that reached p<.05.
Finally, we tested if the valence of nodes in the networks affected the results by examining whether the number of negative (vs. positive) EMA items in a given network influenced the association between depression and network connectivity.
Network Stability Test.In order to estimate each network's stability, we successively dropped, within-individual, up to 85% of each participant's data in increments of 5% and calculated the within-individual correlation between network structure among the full dataset and the down sampled data.For each participant, we calculated the proportion of data that could be dropped to retain a correlation of 0.70 with their full timeseries, i.e., correlation stability coefficient (CS coefficient).Based on previously recommended guidelines(1), the correlation stability coefficient should not be below 0.25 and ideally above 0.50.After examining internal reliability, we tested the generalisability of the structure of these networks across datasets by correlating the edge strengths (25 edges for the directed network, and 10 for the contemporaneous network) across the independent samples.Regularised Networks.Regularising individual networks prior to averaging them is in our opinion inappropriate, as it zero-inflates the data and removes the benefit obtained from averaging across many networks.However, for the interested reader, we repeated our main analyses using regularised individual networks, that are then averaged over to derive a mean network connectivity score per participant.We estimated 100 5-node personalised regularised networks using the graphicalVAR package (nLambda = 50, gamma = 0).We chose to estimate a random subset of 100 5-node networks, instead of the full 4,368 5-node networks, due to the computational intensity involved with estimating regularised networks.
Like with non-regularised networks, we tested the association between mean regularised contemporaneous and directed network connectivity and i) baseline depression and ii) 8week depression variability.
Full-16 Node Network.As a final step, we decided to check whether the results from a single full 16-node network would be similar to those obtained from the average of all 4,368 5-node networks.In both samples, we estimated a single 16-node network once for each participant and then correlated the resultant network connectivity (contemporaneous and directed) with baseline depression and depression variance.
Observed Power from Experiment 1.A power analysis was completed in R using the pwr package with alpha=0.05,power=0.8.The observed associations between network connectivity and baseline depression were relatively small and varied across our two samples.Before carrying out Experiment 1, we estimated power for detecting associations between the connectivity of both contemporaneous and directed networks and mean and variance of depression.The average Pearson R between baseline depression and mean contemporaneous network connectivity across Paid Students and Citizen Scientists was r=.15 (i.e., mean PS: r=.07 and CS: r=.23).At an alpha of p=.05, power of 0.8, a sample of N=323 is required to detect this.The same average for directed network connectivity was r=.11 (i.e., mean PS: r=.03 and CS: r=.19).A sample of N=645 would be required to detect this effect.In Experiment 2, the available sample size of our external dataset (HNATD) was N=519 and we were thus powered to test for contemporaneous network associations only.
The effect size for the observed association between network connectivity and SD of depression was much stronger in both samples and for both network types (contemporaneous and directed).The average Pearson R between SD of depression and mean contemporaneous network connectivity across Paid Students and Citizen Scientists was r=.345 (i.e., mean PS: r=.29 and CS: r=.40).At an alpha of p=.05, power of 0.8, a sample of N=63 is required to detect this.The same average for directed network connectivity was r=.26 (i.e., mean PS: r=.21 and CS: r=.31).At an alpha of p=.05, power of 0.8, a sample of N=113 is required to detect this.In Experiment 2, the sample size of the HNATD was therefore well-powered to detect associations between network connectivity and variance in depressive symptomology.

Citizen Science sample (HNATD)
Procedure.At baseline, participants completed the Quick Inventory of Depressive Symptoms (QIDS) (2).Participants rated their mood on a visual analogue scale between 0 (not at all) -100 (very much).Among the 43 questions that participants answered, we selected a subset of 11 items (6 positive and 5 negative) from the Positive and Negative Affect Schedule (PANAS).Items selected were relaxed, energetic, anxious, enthusiastic, nervous, content, irritable, calm, dull, cheerful, and tired.There were 1,302 participants who completed at least 1 EMA assessment and 519 that completed at least 75% of assessments and were analysed here (Table S1).We retained the 'gloomy' for a separate analysis described below.
Data Preparation.As with the Experiment 1 Neureka datasets, positive items were first reverse coded to reflect negative affect, e.g., enthusiastic became 'not enthusiastic'.The HNATD study used VAS scales from 0-100 and so variance in responding was not an issue for network estimation.Nonetheless, we added the same noise to the data as in Experiment 1 for consistency.More importantly perhaps, HNATD implemented a different EMA schedule to the Neureka studies; they had 3 assessments per day, resulting in unequal intervals between assessments overnight.To avoid this, we removed all overnight lags in our analyses, so the directed network edges uniformly reflect the 6-hour lag of one emotion onto another (and itself).
As a final step, we evaluated two key assumptions VAR models which are that the underlying data is both stationary and homoscedastic.We tested the individual time-series in both samples for stationarity using the KPSS test and found the majority of data (65%) were stationary.We also tested each time-series for homoscedasticity, using the Breusch-Pagan test, and found the majority of data (72%) were homoscedastic.Data Analysis.Analysis was carried out in an identical manner to that described in Experiment 1.Because we do not have weekly depression scores, we used the standard deviation of the EMA item 'gloomy' as a proxy for the variance in depression and used it to test if network connectivity was best explained by variance in negative emotion, rather than its point-estimated severity per se.Note that the 'gloomy' item was not used in any network estimation analysis.For permutation analyses, as with the Neureka samples, participants classified as outliers in terms of their network connectivity scores were excluded on a pernetwork basis.The mean sample size in each network was therefore N=515.3,min=512, max=516.

Methods for Experiment 3: Comparison of Clinical and Non-Clinical Network Structure and Stability
Procedure.Data were gathered from 45 patients with a primary diagnosis of MDD or GAD who completed EMA for 30 days prior to undergoing therapy (3).At baseline, participants completed the Hamilton Anxiety Rating Scale (HARS)(4) and the Hamilton Rating Scale for Depression (HRSD) (5).Participants were prompted to complete EMA assessments 4 times per day for a minimum of 30 days and rated items on a visual analogue scale from 0 (not at all) to 100 (as much as possible).For each assessment, participants answered 26 questions related to symptoms of MDD, GAD, positive affect, negative affect, rumination, behavioural avoidance, and reassurance seeking.We selected only the positive and negative affect items, and removed items that were either behavioural, e.g., procrastinated, or physical, e.g., experienced muscle fatigue.After removing these questions, we were left with 17 items including: energetic, enthusiastic, content, irritable, restless, worried, worthless or guilty, frightened or afraid, loss of interest or pleasure, angry, hopeless, positive, fatigued, difficulty concentrating, accepted or supported, threatened, and dwelled on the past (Table S2).The 'down or depressed' item was excluded from network estimation but was retained for a separate analysis detailed below.Data Preparation.No exclusions were applied to this dataset, as all participants provided to us had completed the study requirement of 30 days with at least one assessment per day (Table S1).As with in Experiment 1 and 2, positive items were reverse coded, and we added a small amount of noise to the data to ensure networks were estimable.Similar to HNATD, we removed all overnight lags in our analyses so that all directed edges referred to a 4-hour interval of one emotion onto another.Data Analysis.Analysis was carried out in an identical manner to that described in Experiment 1 and 2. Because only baseline depression scores were available, we used the standard deviation of the 'down or depressed' item over the entire time-series to approximate depression variance, as a proxy for the weekly depression scores available in the Neureka samples.We then estimated the correlation between network connectivity and the variance of 'down or depressed'.
For permutation analyses, participants classified as outliers in terms of their network connectivity scores were also excluded on a per-network basis.The mean sample size in each network was therefore N=44.9, min=44, max=45.The "Other" gender category includes: non-binary, transgender male, transgender female, and did not disclose.Percent depressed refers to the percentage of a given sample on or above threshold for depression on the respective self-report instrucment used in that study.For neureka datasets that was ≥ 20 on the CES-D, for HNATD > 5 on the QIDS, and for the clinical sample was > 7 on the HAM-D.
*Participants in Fisher et al., (2017) were required to complete a fixed number of assessments, but were asked to complete at least 1 assessment for 30 days.We repeated our main analyses with regularised networks as a sensitivity analysis.
Consistent with the main results, there was a significant association between per-participant mean regularised contemporaneous network connectivity and baseline depression in Citizen Scientists (r = 0.16, p < 0.05) but not Paid Students (r = 0.07, p = 0.39) (Figure S7A).
However, there was no significant association between per-participant mean regularised directed network connectivity and baseline depression in either sample (r = 0.02, p > 0.05) (Figure S7B).Moving on to 8-week depression variability, there was a significant association between per-participant mean regularised contemporaneous network connectivity and depression variability in both Paid Students (r = 0.32, p < 0.001) and Citizen Scientists (r = 0.38, p < 0.001).While regularised directed network connectivity was significantly associated with depression variability in Citizen Scientists (r = 0.16, p < 0.05) but not Paid Students (r = 0.11, p = 0.27).For contemporaneous networks, the pattern of significant associations with depression (baseline and variability) was the same regardless of network type (i.e., regularised vs. non-regularised).As expected, the impact of regularisation was more prominent in directed networks where zero inflation was evident.
Lastly, we evaluated the similarity in results between a single 16-node network and the average of 4,368 networks.In both samples, for both contemporaneous and directed networks, there was a significant association between network connectivity and depression variance (p < 0.05) (Table S5).Baseline depression was significantly positively associated with contemporaneous network connectivity in citizen scientists (β = 0.23, p = 0.001), but not paid students (Beta = 0.08, p = 0.33) Unlike the association with average network connectivity from 4,368 5-node networks, baseline depression was not significantly associated with directed connectivity connected from the full 16-node network (β = 0.05, p = 0.45).Overall, the results between the two approaches are very similar, but we decided to retain the main analyses with the average of 4,368 5-node networks because it reduces collinearity among items.Since this could affect the interpretation of observed edges between nodes.

Figure S1 :
Figure S1: Association of per-participant network connectivity and Week-8 depression.

Figure S2 :
Figure S2: Comparative analysis of strength of association (x-axis, beta) between network connectivity and 8-week depression variance and other metrics derived from EMA (Mean, Standard Deviation [SD] and autoregression [AR]).

Figure S3 .
Figure S3.Replication of results using 'mean depression' instead of baseline depression.

Figure S5 :
Figure S5: Exemplar network stability in a clinical sample

Figure S6 :
Figure S6: Impact of node valence on relationship between network connectivity and depression variability

Figure S7 .
Figure S7.Regularised Contemporaneous and Directed Network Connectivity in Paid Students and Citizen Scientists.

Figure S8 .
Figure S8.Depression variability is positively related to regularised network connectivity

Table S3 : Association between network connectivity in the exemplar 5- node network with SD depression (Paid Students & Citizen Scientists), SD gloomy (HNATD), and SD down or depressed (Clinical Sample).
In the unadjusted model, symptom variability ~ network connectivity, while the adjusted model individual network stability is added as a covariate, i.e., symptom variability ~ network connectivity + network stability.Overall, adjusting for network stability had minimal effect on the relationship between network connectivity and symptom variability.

Table S6 : Proportion of variance explained for models predicting depression variance in Paid Students and Citizen Scientists.
Contemporaneous network connectivity explained the second and third highest proportions of variance in depression variability in Paid Students and Citizen Scientists respectively.Given the overlap between depression variability and EMA SD, it is unsurprising that the EMA SD model explained the greatest proportion of variance.Compared to contemporaneous network connectivity, but also other measures of affect variability, directed network connectivity was a comparatively weak predictor of depression variability.In Paid Students, the directed network connectivity model explained the least variance in depression variability.While it was second to last performing model in the Citizen Scientist sample.